
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
cols = px.colors.DEFAULT_PLOTLY_COLORS


TopProduction = pd.read_csv("TopProductionMethods.csv")
TopDistributors = pd.read_csv("TopDistributors.csv")
TopGenres = pd.read_csv("TopGenres.csv")
PopularTypes = pd.read_csv("PopularCreativeTypes.csv")
TopGrossingRatings = pd.read_csv("TopGrossingRatings.csv")
TopGrossingSources = pd.read_csv("TopGrossingSources.csv")
HighestGrossers =  pd.read_csv("HighestGrossers.csv")
AnnualTicketSales = pd.read_csv("AnnualTicketSales.csv")
WideReleasesCount = pd.read_csv("WideReleasesCount.csv")


TopProduction["MARKET SHARE"] = TopProduction["MARKET SHARE"].str.replace('%','').astype("float64")
TopProduction[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopProduction[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].replace({"\$":'',',':''},regex=True)
TopProduction[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopProduction[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].astype('float64')


TopProduction


TopDistributors


TopDistributors["MARKET SHARE"] = TopDistributors["MARKET SHARE"].str.replace('%','').astype("float64")
TopDistributors[["TOTAL GROSS","AVERAGE GROSS"]] = TopDistributors[["TOTAL GROSS","AVERAGE GROSS"]].replace({"\$":'',',':''},regex=True)
TopDistributors[["TOTAL GROSS","AVERAGE GROSS"]] = TopDistributors[["TOTAL GROSS","AVERAGE GROSS"]].astype('float64')


TopDistributors


TopGenres


TopGenres["MARKET SHARE"] = TopGenres["MARKET SHARE"].str.replace('%','').astype("float64")
TopGenres[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopGenres[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].replace({"\$":'',',':''},regex=True)
TopGenres[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopGenres[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].astype('float64')


TopGenres


PopularTypes


PopularTypes["MARKET SHARE"] = PopularTypes["MARKET SHARE"].str.replace('%','').astype("float64")
PopularTypes[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = PopularTypes[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].replace({"\$":'',',':''},regex=True)
PopularTypes[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = PopularTypes[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].astype('float64')


PopularTypes = PopularTypes.dropna(axis=0) #Remove NAN row
PopularTypes


TopGrossingRatings


TopGrossingRatings["MARKET SHARE"] = TopGrossingRatings["MARKET SHARE"].str.replace('%','').astype("float64")
TopGrossingRatings[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopGrossingRatings[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].replace({"\$":'',',':''},regex=True)
TopGrossingRatings[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopGrossingRatings[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].astype('float64')


TopGrossingSources


TopGrossingSources["MARKET SHARE"] = TopGrossingSources["MARKET SHARE"].str.replace('%','').astype("float64")
TopGrossingSources[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopGrossingSources[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].replace({"\$":'',',':''},regex=True)
TopGrossingSources[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]] = TopGrossingSources[["TOTAL GROSS","AVERAGE GROSS","MOVIES"]].astype('float64')


TopGrossingSources


HighestGrossers =  pd.read_csv("HighestGrossers.csv")
HighestGrossers


HighestGrossers["TICKETS SOLD"] = HighestGrossers["TICKETS SOLD"].str.replace(',','').astype("int64")
HighestGrossers[["TOTAL FOR YEAR","TOTAL IN 2019 DOLLARS"]] = HighestGrossers[["TOTAL FOR YEAR","TOTAL IN 2019 DOLLARS"]].replace({"\$":'',',':''},regex=True)
HighestGrossers[["TOTAL FOR YEAR","TOTAL IN 2019 DOLLARS"]] = HighestGrossers[["TOTAL FOR YEAR","TOTAL IN 2019 DOLLARS"]].astype('float64')


HighestGrossers=HighestGrossers.dropna()


AnnualTicketSales.drop("Unnamed: 5", axis = 1,inplace=True)
AnnualTicketSales


AnnualTicketSales["TICKETS SOLD"] = AnnualTicketSales["TICKETS SOLD"].str.replace(',','').astype("int64")
AnnualTicketSales[["TOTAL BOX OFFICE","TOTAL INFLATION ADJUSTED BOX OFFICE", "AVERAGE TICKET PRICE"]] = AnnualTicketSales[["TOTAL BOX OFFICE","TOTAL INFLATION ADJUSTED BOX OFFICE", "AVERAGE TICKET PRICE"]].replace({"\$":'',',':''},regex=True)
AnnualTicketSales[["TOTAL BOX OFFICE","TOTAL INFLATION ADJUSTED BOX OFFICE", "AVERAGE TICKET PRICE"]] = AnnualTicketSales[["TOTAL BOX OFFICE","TOTAL INFLATION ADJUSTED BOX OFFICE", "AVERAGE TICKET PRICE"]].astype('float64')


AnnualTicketSales


WideReleasesCount.drop("Unnamed: 9", axis=1, inplace=True)
WideReleasesCount


TopProduction.head()


c = dict(zip(TopProduction["PRODUCTION METHODS"].unique(), px.colors.qualitative.G10))
fig =  make_subplots(1, 2,  specs=[[{"type": "xy"},{"type": "domain"}]],subplot_titles=['Average Gross', 'Market Share'])
fig.add_trace(
go.Bar(x = TopProduction['PRODUCTION METHODS'], y = TopProduction['AVERAGE GROSS'], showlegend = False, marker=dict(color=['#1f77b4','#d62728','#2ca02c','#9467bd','#ff7f0e','#17becf','#e377c2'])),1,1
)
fig.add_trace(
go.Pie(labels=TopProduction['PRODUCTION METHODS'], values=TopProduction['MARKET SHARE']),1,2
)
fig.update_layout(title_text="Average Gross and Market Shares of different production types for movies")
fig.show()


TopDistributors


c = dict(zip(TopDistributors["DISTRIBUTORS"].unique(), px.colors.qualitative.G10))
fig =  make_subplots(1, 2,  specs=[[{"type": "xy"},{"type": "domain"}]],subplot_titles=['Average Gross', 'Market Share'])
fig.add_trace(
go.Bar(x = TopDistributors['DISTRIBUTORS'], y = TopDistributors['AVERAGE GROSS'], showlegend = False, marker=dict(color=['#1f77b4','#d62728','#2ca02c','#9467bd','#ff7f0e','#17becf','#e97a96','#b1ff65','#ff63e9','#FFCD9B'])),1,1
)
fig.add_trace(
go.Pie(labels=TopDistributors['DISTRIBUTORS'], values=TopDistributors['MARKET SHARE']),1,2
)
fig.update_layout(title_text="Average Gross and Market Shares of different distributors")
fig.show()


TopGenres


c = dict(zip(TopGenres["GENRES"].unique(), px.colors.qualitative.G10))
fig =  make_subplots(1, 2,  specs=[[{"type": "xy"},{"type": "domain"}]],subplot_titles=['Average Gross', 'Market Share'])
fig.add_trace(
go.Bar(x = TopGenres['GENRES'], y = TopGenres['AVERAGE GROSS'], showlegend = False, marker=dict(color=['#1f77b4','#d62728','#2ca02c','#9467bd','#ff7f0e','#17becf','#e97a96','#b1ff65','#ff63e9','#FFCD9B'])),1,1
)
fig.add_trace(
go.Pie(labels=TopGenres['GENRES'], values=TopGenres['MARKET SHARE']),1,2
)
fig.update_layout(title_text="Average Gross and Market Shares of different movie genres")
fig.show()


PopularTypes


c = dict(zip(PopularTypes["CREATIVE TYPES"].unique(), px.colors.qualitative.G10))
fig =  make_subplots(1, 2,  specs=[[{"type": "xy"},{"type": "domain"}]],subplot_titles=['Average Gross', 'Market Share'])
fig.add_trace(
go.Bar(x = PopularTypes['CREATIVE TYPES'], y = PopularTypes['AVERAGE GROSS'], showlegend = False, marker=dict(color=['#1f77b4','#d62728','#2ca02c','#9467bd','#ff7f0e','#17becf','#e97a96','#b1ff65','#ff63e9','#FFCD9B'])),1,1
)
fig.add_trace(
go.Pie(labels=PopularTypes['CREATIVE TYPES'], values=PopularTypes['MARKET SHARE']),1,2
)
fig.update_layout(title_text="Average Gross and Market Shares of different movie types")
fig.show()


TopGrossingRatings


c = dict(zip(TopGrossingRatings["MPAA RATINGS"].unique(), px.colors.qualitative.G10))
fig =  make_subplots(1, 2,  specs=[[{"type": "xy"},{"type": "domain"}]],subplot_titles=['Average Gross', 'Market Share'])
fig.add_trace(
go.Bar(x = TopGrossingRatings['MPAA RATINGS'], y = TopGrossingRatings['AVERAGE GROSS'], showlegend = False, marker=dict(color=['#1f77b4','#d62728','#2ca02c','#9467bd','#ff7f0e','#17becf','#e97a96','#b1ff65','#ff63e9','#FFCD9B'])),1,1
)
fig.add_trace(
go.Pie(labels=TopGrossingRatings['MPAA RATINGS'], values=TopGrossingRatings['MARKET SHARE']),1,2
)
fig.update_layout(title_text="Average Gross and Market Shares of different movies based on ratings")
fig.show()


TopGrossingSources


c = dict(zip(TopGrossingSources["SOURCES"].unique(), px.colors.qualitative.G10))
fig =  make_subplots(1, 2,  specs=[[{"type": "xy"},{"type": "domain"}]],subplot_titles=['Average Gross', 'Market Share'])
fig.add_trace(
go.Bar(x = TopGrossingSources['SOURCES'], y = TopGrossingSources['AVERAGE GROSS'], showlegend = False, marker=dict(color=['#1f77b4','#d62728','#2ca02c','#9467bd','#ff7f0e','#17becf','#e97a96','#b1ff65','#ff63e9','#FFCD9B'])),1,1
)
fig.add_trace(
go.Pie(labels=TopGrossingSources['SOURCES'], values=TopGrossingSources['MARKET SHARE']),1,2
)
fig.update_layout(title_text="Average Gross and Market Shares of different sources of movies (i.e, originals, spinoffs, remakes)")
fig.show()


HighestGrossers


fig = px.treemap(HighestGrossers, path=[px.Constant("all"), 'DISTRIBUTOR', 'MPAA RATING', 'GENRE'], values='TOTAL IN 2019 DOLLARS')
fig.update_traces(root_color="lightgrey")
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()


fig = go.Figure(data=[
    go.Bar(name='Total Revenue',x=HighestGrossers['MPAA RATING'], y=HighestGrossers['TOTAL FOR YEAR']),
    go.Bar(name='Total Revenue adjusted for inflation', x=HighestGrossers['MPAA RATING'], y=HighestGrossers['TOTAL IN 2019 DOLLARS'])
])
fig.update_layout(barmode='group')
fig.update_layout(title_text="Total Revenue and Total Revenue adjusted for inflation sorted by Distributor")
fig.show()


AnnualTicketSales


fig = make_subplots(specs=[[{"secondary_y": True}]])


fig.add_trace(
    go.Scatter(x=AnnualTicketSales["YEAR"], y=AnnualTicketSales["TICKETS SOLD"], name="yaxis data"),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter(x=AnnualTicketSales["YEAR"], y=AnnualTicketSales["AVERAGE TICKET PRICE"], name="yaxis2 data"),
    secondary_y=True,
)

# Add figure title
fig.update_layout(
    title_text="Tickets sold and average ticket price thorughout the years"
)

# Set x-axis title
fig.update_xaxes(title_text="Years")

# Set y-axes titles
fig.update_yaxes(title_text="<b>Tickets Sold</b>", secondary_y=False)
fig.update_yaxes(title_text="<b>Average Ticket Price</b> ", secondary_y=True)

fig.show()


fig = make_subplots(specs=[[{"secondary_y": True}]])


fig.add_trace(
    go.Scatter(x=AnnualTicketSales["YEAR"], y=AnnualTicketSales["TOTAL INFLATION ADJUSTED BOX OFFICE"], name="yaxis data"),
    secondary_y=False,
)

fig.update_layout(
    title_text="Total inflation adjusted box office over the years"
)

fig.update_xaxes(title_text="Years")
fig.add_vline(x="2019.25")
fig.update_yaxes(title_text="<b>Tickets Sold</b>", secondary_y=False)
fig.update_yaxes(title_text="<b>Average Ticket Price</b> ", secondary_y=True)

fig.show()


WideReleasesCount


fig =  make_subplots(4, 2,  specs=[[{"type": "xy"},{"type": "xy"}],[{"type": "xy"},{"type": "xy"}],[{"type": "xy"},{"type": "xy"}],[{"type": "xy"},{"type": "xy"}]],subplot_titles=("Warner Brothers","Walt Disney", "20th Century Fox", "Paramount Pictures", "Sony Pictures","Universal","Total Major 6", "Total Other Studios"))
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['WARNER BROS'], name="Warner Bros", showlegend = False),1,1
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['WALT DISNEY'], name="Walt Disney",showlegend = False),1,2
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['20TH CENTURY FOX'], name="20TH Century FOx",showlegend = False),2,1
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['PARAMOUNT PICTURES'],name="Paramount Pictures", showlegend = False),2,2
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['SONY PICTURES'], name="Sony Pictures",showlegend = False),3,1
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['UNIVERSAL'], name="Universal",showlegend = False),3,2
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['TOTAL MAJOR 6'], name="Total Major 6",showlegend = False),4,1
)
fig.add_trace(
go.Bar(x = WideReleasesCount['YEAR'], y = WideReleasesCount['TOTAL OTHER STUDIOS'], name="Total Other Studios" ,showlegend = False),4,2
)
fig.update_layout(height=900, width=900,
                  title_text='Wide Releases over the years sorted by studio')

fig.show()

	RANK	PRODUCTION METHODS	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Live Action	14613.0	1.796372e+11	12292972.0	75.56
1	2	Animation/Live Action	264.0	3.034662e+10	114949327.0	12.76
2	3	Digital Animation	365.0	2.392018e+10	65534741.0	10.06
3	4	Hand Animation	164.0	2.960497e+09	18051814.0	1.25
4	5	Stop-Motion Animation	37.0	6.764901e+08	18283517.0	0.28
5	6	Multiple Production Methods	26.0	4.372830e+07	1681858.0	0.02
6	7	Rotoscoping	4.0	8.468385e+06	2117096.0	0.00

	RANK	DISTRIBUTORS	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Walt Disney	588	$40,472,424,278	$68,830,654	17.02%
1	2	Warner Bros.	824	$36,269,425,479	$44,016,293	15.25%
2	3	Sony Pictures	747	$29,113,002,302	$38,973,229	12.24%
3	4	Universal	535	$28,089,932,569	$52,504,547	11.81%
4	5	20th Century Fox	525	$25,857,839,756	$49,253,028	10.88%
5	6	Paramount Pictures	493	$24,361,425,304	$49,414,656	10.25%
6	7	Lionsgate	426	$9,631,837,781	$22,609,948	4.05%
7	8	New Line	209	$6,195,268,024	$29,642,431	2.61%
8	9	Dreamworks SKG	77	$4,278,649,271	$55,566,874	1.80%
9	10	Miramax	385	$3,836,019,208	$9,963,686	1.61%

	RANK	DISTRIBUTORS	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Walt Disney	588	4.047242e+10	68830654.0	17.02
1	2	Warner Bros.	824	3.626943e+10	44016293.0	15.25
2	3	Sony Pictures	747	2.911300e+10	38973229.0	12.24
3	4	Universal	535	2.808993e+10	52504547.0	11.81
4	5	20th Century Fox	525	2.585784e+10	49253028.0	10.88
5	6	Paramount Pictures	493	2.436143e+10	49414656.0	10.25
6	7	Lionsgate	426	9.631838e+09	22609948.0	4.05
7	8	New Line	209	6.195268e+09	29642431.0	2.61
8	9	Dreamworks SKG	77	4.278649e+09	55566874.0	1.80
9	10	Miramax	385	3.836019e+09	9963686.0	1.61

	RANK	GENRES	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Adventure	1,102	$64,529,536,530	$58,556,748	27.14%
1	2	Action	1,098	$49,339,974,493	$44,936,224	20.75%
2	3	Drama	5,479	$35,586,177,269	$6,495,013	14.97%
3	4	Comedy	2,418	$33,687,992,318	$13,932,172	14.17%
4	5	Thriller/Suspense	1,186	$19,810,201,102	$16,703,374	8.33%
5	6	Horror	716	$13,430,378,699	$18,757,512	5.65%
6	7	Romantic Comedy	630	$10,480,124,374	$16,635,118	4.41%
7	8	Musical	201	$4,293,988,317	$21,363,126	1.81%
8	9	Documentary	2,415	$2,519,513,142	$1,043,277	1.06%
9	10	Black Comedy	213	$2,185,433,323	$10,260,250	0.92%

	RANK	GENRES	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Adventure	1102.0	6.452954e+10	58556748.0	27.14
1	2	Action	1098.0	4.933997e+10	44936224.0	20.75
2	3	Drama	5479.0	3.558618e+10	6495013.0	14.97
3	4	Comedy	2418.0	3.368799e+10	13932172.0	14.17
4	5	Thriller/Suspense	1186.0	1.981020e+10	16703374.0	8.33
5	6	Horror	716.0	1.343038e+10	18757512.0	5.65
6	7	Romantic Comedy	630.0	1.048012e+10	16635118.0	4.41
7	8	Musical	201.0	4.293988e+09	21363126.0	1.81
8	9	Documentary	2415.0	2.519513e+09	1043277.0	1.06
9	10	Black Comedy	213.0	2.185433e+09	10260250.0	0.92

	RANK	CREATIVE TYPES	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1.0	Contemporary Fiction	7,442	$96,203,727,036	$12,927,133	40.46%
1	2.0	Kids Fiction	564	$32,035,539,746	$56,800,602	13.47%
2	3.0	Science Fiction	724	$29,922,660,857	$41,329,642	12.59%
3	4.0	Fantasy	759	$21,724,062,575	$28,621,953	9.14%
4	5.0	Super Hero	129	$20,273,157,911	$157,156,263	8.53%
5	6.0	Historical Fiction	1,487	$18,521,260,744	$12,455,454	7.79%
6	7.0	Dramatization	1,175	$15,715,191,699	$13,374,631	6.61%
7	8.0	Factual	2,467	$2,960,327,207	$1,199,970	1.25%
8	9.0	Multiple Creative Types	42	$117,574,526	$2,799,393	0.05%
9	NaN	NaN	NaN	NaN	NaN	NaN

	RANK	CREATIVE TYPES	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1.0	Contemporary Fiction	7442.0	9.620373e+10	12927133.0	40.46
1	2.0	Kids Fiction	564.0	3.203554e+10	56800602.0	13.47
2	3.0	Science Fiction	724.0	2.992266e+10	41329642.0	12.59
3	4.0	Fantasy	759.0	2.172406e+10	28621953.0	9.14
4	5.0	Super Hero	129.0	2.027316e+10	157156263.0	8.53
5	6.0	Historical Fiction	1487.0	1.852126e+10	12455454.0	7.79
6	7.0	Dramatization	1175.0	1.571519e+10	13374631.0	6.61
7	8.0	Factual	2467.0	2.960327e+09	1199970.0	1.25
8	9.0	Multiple Creative Types	42.0	1.175745e+08	2799393.0	0.05

	RANK	MPAA RATINGS	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	PG-13	3,243	$113,524,789,243	$35,006,102	47.75%
1	2	R	5,480	$63,497,164,978	$11,587,074	26.71%
2	3	PG	1,535	$49,124,317,794	$32,002,813	20.66%
3	4	G	395	$9,572,240,391	$24,233,520	4.03%
4	5	Not Rated	5,820	$1,918,358,283	$329,615	0.81%
5	6	NC-17	24	$44,850,139	$1,868,756	0.02%
6	7	Open	5	$5,489,687	$1,097,937	0.00%
7	8	GP	7	$552,618	$78,945	0.00%

	RANK	SOURCES	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Original Screenplay	7,946	$106,375,196,782	$13,387,264	44.74%
1	2	Based on Fiction Book/Short Story	2,150	$47,005,613,207	$21,863,076	19.77%
2	3	Based on Comic/Graphic Novel	249	$23,369,989,130	$93,855,378	9.83%
3	4	Remake	328	$12,832,659,970	$39,123,963	5.40%
4	5	Based on Real Life Events	3,225	$11,398,356,297	$3,534,374	4.79%
5	6	Based on TV	231	$11,305,006,312	$48,939,421	4.75%
6	7	Based on Factual Book/Article	295	$7,443,681,990	$25,232,820	3.13%
7	8	Spin-Off	41	$3,833,128,331	$93,490,935	1.61%
8	9	Based on Folk Tale/Legend/Fairytale	78	$3,406,118,495	$43,668,186	1.43%
9	10	Based on Play	271	$2,111,190,923	$7,790,372	0.89%

	RANK	SOURCES	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	Original Screenplay	7946.0	1.063752e+11	13387264.0	44.74
1	2	Based on Fiction Book/Short Story	2150.0	4.700561e+10	21863076.0	19.77
2	3	Based on Comic/Graphic Novel	249.0	2.336999e+10	93855378.0	9.83
3	4	Remake	328.0	1.283266e+10	39123963.0	5.40
4	5	Based on Real Life Events	3225.0	1.139836e+10	3534374.0	4.79
5	6	Based on TV	231.0	1.130501e+10	48939421.0	4.75
6	7	Based on Factual Book/Article	295.0	7.443682e+09	25232820.0	3.13
7	8	Spin-Off	41.0	3.833128e+09	93490935.0	1.61
8	9	Based on Folk Tale/Legend/Fairytale	78.0	3.406118e+09	43668186.0	1.43
9	10	Based on Play	271.0	2.111191e+09	7790372.0	0.89

	YEAR	MOVIE	GENRE	MPAA RATING	DISTRIBUTOR	TOTAL FOR YEAR	TOTAL IN 2019 DOLLARS	TICKETS SOLD
0	1995	Batman Forever	Drama	PG-13	Warner Bros.	$184,031,112	$387,522,978	4,23,06,002
1	1996	Independence Day	Adventure	PG-13	20th Century Fox	$306,169,255	$634,504,608	6,92,69,062
2	1997	Men in Black	Adventure	PG-13	Sony Pictures	$250,650,052	$500,207,943	5,46,07,854
3	1998	Titanic	Adventure	PG-13	Paramount Pictures	$443,319,081	$865,842,808	9,45,24,324
4	1999	Star Wars Ep. I: The Phantom Menace	Adventure	PG	20th Century Fox	$430,443,350	$776,153,749	8,47,32,942
5	2000	How the Grinch Stole Christmas	Adventure	PG	Universal	$253,367,455	$430,583,644	4,70,06,948
6	2001	Harry Potter and the Sorcerer’s Stone	Adventure	PG	Warner Bros.	$300,404,434	$486,166,890	5,30,74,988
7	2002	Spider-Man	Adventure	PG-13	Sony Pictures	$403,706,375	$636,480,273	6,94,84,746
8	2003	Finding Nemo	Adventure	G	Walt Disney	$339,714,367	$516,050,346	5,63,37,374
9	2004	Shrek 2	Adventure	PG	Dreamworks SKG	$441,226,247	$650,826,473	7,10,50,925
10	2005	Star Wars Ep. III: Revenge of the Sith	Action	PG-13	20th Century Fox	$380,270,577	$543,413,171	5,93,24,582
11	2006	Pirates of the Caribbean: Dead Man’s Chest	Action	PG-13	Walt Disney	$423,315,812	$591,995,851	6,46,28,368
12	2007	Spider-Man 3	Adventure	PG-13	Sony Pictures	$336,530,303	$448,054,878	4,89,14,288
13	2008	The Dark Knight	Adventure	PG-13	Warner Bros.	$531,001,578	$677,433,772	7,39,55,652
14	2009	Transformers: Revenge of the Fallen	Action	PG-13	Paramount Pictures	$402,111,870	$491,112,631	5,36,14,916
15	2010	Toy Story 3	Action	G	Walt Disney	$415,004,880	$481,805,411	5,25,98,844
16	2011	Harry Potter and the Deathly Hallows: Part II	Action	PG-13	Warner Bros.	$381,011,219	$440,108,798	4,80,46,812
17	2012	The Avengers	Adventure	PG-13	Walt Disney	$623,357,910	$717,331,462	7,83,11,295
18	2013	Iron Man 3	Adventure	PG-13	Walt Disney	$408,992,272	$460,808,016	5,03,06,552
19	2014	Guardians of the Galaxy	Adventure	PG-13	Walt Disney	$333,055,258	$373,413,235	4,07,65,637
20	2015	Star Wars Ep. VII: The Force Awakens	Action	PG-13	Walt Disney	$742,208,942	$806,480,887	8,80,43,765
21	2016	Finding Dory	Action	PG	Walt Disney	$486,295,561	$514,967,322	5,62,19,140
22	2017	Star Wars Ep. VIII: The Last Jedi	Action	PG-13	Walt Disney	$517,218,368	$528,173,936	5,76,60,910
23	2018	Black Panther	Action	PG-13	Walt Disney	$700,059,566	$703,901,821	7,68,45,177
24	2019	Avengers: Endgame	NaN	PG-13	Walt Disney	$858,373,000	$858,373,002	9,37,08,843
25	2020	Bad Boys For Life	NaN	R	Sony Pictures	$204,417,855	$204,417,848	2,23,16,359
26	2021	Shang-Chi and the Legend of the Ten Rings	NaN	PG-13	Walt Disney	$224,226,704	$224,226,704	2,44,78,897

	YEAR	TICKETS SOLD	TOTAL BOX OFFICE	TOTAL INFLATION ADJUSTED BOX OFFICE	AVERAGE TICKET PRICE
0	2021	42,37,74,881	$3,881,777,912	$3,881,777,912	$9.16
1	2020	22,36,38,958	$2,048,534,616	$2,048,534,616	$9.16
2	2019	1,22,85,41,629	$11,253,443,955	$11,253,444,050	$9.16
3	2018	1,31,15,36,128	$11,948,096,650	$12,013,670,952	$9.11
4	2017	1,22,56,39,761	$10,993,991,460	$11,226,860,216	$8.97
5	2016	1,30,25,56,378	$11,267,115,924	$11,931,416,424	$8.65
6	2015	1,32,33,56,776	$11,155,900,636	$12,121,948,075	$8.43
7	2014	1,25,74,02,920	$10,272,985,008	$11,517,810,744	$8.17
8	2013	1,33,91,68,926	$10,887,446,341	$12,266,787,382	$8.13
9	2012	1,38,09,21,942	$10,992,141,616	$12,649,244,986	$7.96
10	2011	1,28,29,15,168	$10,173,519,704	$11,751,502,955	$7.93
11	2010	1,32,85,49,021	$10,482,254,025	$12,169,509,032	$7.89
12	2009	1,41,85,67,388	$10,639,257,284	$12,994,051,137	$7.50
13	2008	1,35,80,42,073	$9,750,744,148	$12,439,665,380	$7.18
14	2007	1,42,00,36,680	$9,769,854,914	$13,007,535,993	$6.88
15	2006	1,39,87,38,283	$9,161,738,221	$12,812,442,671	$6.55
16	2005	1,37,29,80,280	$8,800,805,718	$12,576,499,367	$6.41
17	2004	1,49,56,51,298	$9,287,996,519	$13,700,165,883	$6.21
18	2003	1,52,45,89,620	$9,193,277,289	$13,965,240,914	$6.03
19	2002	1,57,57,56,527	$9,155,147,215	$14,433,929,789	$5.81
20	2001	1,46,58,74,205	$8,296,849,636	$13,427,407,722	$5.66
21	2000	1,39,74,60,079	$7,532,311,479	$12,800,734,319	$5.39
22	1999	1,44,46,64,086	$7,338,894,852	$13,233,123,027	$5.08
23	1998	1,44,38,32,471	$6,771,575,283	$13,225,505,439	$4.69
24	1997	1,35,73,49,648	$6,230,235,770	$12,433,322,785	$4.59
25	1996	1,30,52,21,290	$5,769,078,886	$11,955,781,912	$4.42
26	1995	1,22,17,05,907	$5,314,421,390	$11,190,826,105	$4.35

	YEAR	TICKETS SOLD	TOTAL BOX OFFICE	TOTAL INFLATION ADJUSTED BOX OFFICE	AVERAGE TICKET PRICE
0	2021	423774881	3.881778e+09	3.881778e+09	9.16
1	2020	223638958	2.048535e+09	2.048535e+09	9.16
2	2019	1228541629	1.125344e+10	1.125344e+10	9.16
3	2018	1311536128	1.194810e+10	1.201367e+10	9.11
4	2017	1225639761	1.099399e+10	1.122686e+10	8.97
5	2016	1302556378	1.126712e+10	1.193142e+10	8.65
6	2015	1323356776	1.115590e+10	1.212195e+10	8.43
7	2014	1257402920	1.027299e+10	1.151781e+10	8.17
8	2013	1339168926	1.088745e+10	1.226679e+10	8.13
9	2012	1380921942	1.099214e+10	1.264924e+10	7.96
10	2011	1282915168	1.017352e+10	1.175150e+10	7.93
11	2010	1328549021	1.048225e+10	1.216951e+10	7.89
12	2009	1418567388	1.063926e+10	1.299405e+10	7.50
13	2008	1358042073	9.750744e+09	1.243967e+10	7.18
14	2007	1420036680	9.769855e+09	1.300754e+10	6.88
15	2006	1398738283	9.161738e+09	1.281244e+10	6.55
16	2005	1372980280	8.800806e+09	1.257650e+10	6.41
17	2004	1495651298	9.287997e+09	1.370017e+10	6.21
18	2003	1524589620	9.193277e+09	1.396524e+10	6.03
19	2002	1575756527	9.155147e+09	1.443393e+10	5.81
20	2001	1465874205	8.296850e+09	1.342741e+10	5.66
21	2000	1397460079	7.532311e+09	1.280073e+10	5.39
22	1999	1444664086	7.338895e+09	1.323312e+10	5.08
23	1998	1443832471	6.771575e+09	1.322551e+10	4.69
24	1997	1357349648	6.230236e+09	1.243332e+10	4.59
25	1996	1305221290	5.769079e+09	1.195578e+10	4.42
26	1995	1221705907	5.314421e+09	1.119083e+10	4.35

	RANK	MPAA RATINGS	MOVIES	TOTAL GROSS	AVERAGE GROSS	MARKET SHARE
0	1	PG-13	3243.0	1.135248e+11	35006102.0	47.75
1	2	R	5480.0	6.349716e+10	11587074.0	26.71
2	3	PG	1535.0	4.912432e+10	32002813.0	20.66
3	4	G	395.0	9.572240e+09	24233520.0	4.03
4	5	Not Rated	5820.0	1.918358e+09	329615.0	0.81
5	6	NC-17	24.0	4.485014e+07	1868756.0	0.02
6	7	Open	5.0	5.489687e+06	1097937.0	0.00
7	8	GP	7.0	5.526180e+05	78945.0	0.00